Parks and recreation systems have evolved in remarkable ways over the past two decades. No longer just playgrounds and ballfields, parks and open spaces have become recognized as essential green infrastructure with the potential to contribute to community resiliency and sustainability. To capitalize on this potential, the parks and recreation system planning process must evolve as well. In Parks and Recreation System Planning, David Barth provides a new, step-by-step approach to creating parks systems that generate greater economic, social, and environmental benefits. Barth first advocates that parks and recreation systems should no longer be regarded as isolated facilities, but as elements of an integrated public realm. Each space should be designed to generate multiple community benefits. Next, he presents a new approach for parks and recreation planning that is integrated into community-wide issues. Chapters outline each step—evaluating existing systems, implementing a carefully crafted plan, and more—necessary for creating a successful, adaptable system. Throughout the book, he describes initiatives that are creating more resilient, sustainable, and engaging parks and recreation facilities, drawing from his experience consulting in more than 100 communities across the U.S. Parks and Recreation System Planning meets the critical need to provide an up-to-date, comprehensive approach for planning parks and recreation systems across the country. This is essential reading for every parks and recreation professional, design professional, and public official who wants their community to thrive.

Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning. Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credit assignment and describe tractable classes of problems for which optimal plans can be derived. This book discusses as well how reactive, integrated systems give rise to new requirements and opportunities for machine learning. The final chapter deals with a method for learning problem decompositions, which is based on an idealized model of efficiency for problem-reduction search. This book is a valuable resource for production managers, planners, scientists, and research workers.